code: def reduce_logmeanexp(input_tensor, axis=None, keep_dims=False):
      logsumexp = tf.reduce_logsumexp(input_tensor, axis, keep_dims)
      input_tensor = tf.convert_to_tensor(input_tensor)
      n = input_tensor.shape.as_list()
      if axis is None:
          n = tf.cast(tf.reduce_prod(n), logsumexp.dtype)
      else:
          n = tf.cast(tf.reduce_prod(n[axis]), logsumexp.dtype)
  
      return -tf.log(n) + logsumexp
  
file_path: edward/examples/iwvi.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import six
  import tensorflow
  from edward.inferences import VariationalInference
  from edward.models import Bernoulli, Normal, RandomVariable
  from edward.util import copy
  from scipy.special import expit
label_desc: Use image node_id 1 for calling a global function with example usage: reduce_logmeanexp(input_tensor, axis, keep_dims) and returns: unknown